AI ethics: where's the line?

AI Ethics

Joe Franklin

Associate Data Literacy and Essentials Manager, DataCamp

The privacy-personalization paradox

  • AI personalizes user experiences, enhancing appeal
  • The privacy-personalization paradox
    • Personalization can compromise user privacy
  • Solution:
    • AI literacy
    • Clear privacy policies
  • Example: Spotify

An image of the Spotify logo.

AI Ethics

The bias-fairness conundrum

  • Bias-fairness conundrum:
    • AI learns from data that can carry societal biases
  • Result:
    • AI may unintentionally amplify these biases
  • Example:
    • Early versions of ChatGPT
  • Solution:
    • Train AI models with fairer, bias-free data

An image of the Chatgpt logo.

AI Ethics

The transparency-complexity trade-off

  • Transparency-complexity trade-off:
    • Complex AI models lack transparency but are highly accurate

An icon depicting a complex algorithm, representing the complexity of AI models.

An image of the AlphaGo logo.

  • Simpler models are more transparent but less accurate
  • AI literacy is vital for comprehension and ethical implications
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AI Ethics

The autonomy-control dilemma

  • Autonomy-control dilemma:

    • AI can act autonomously but might operate outside human control

  • Question:

    • Should we prioritize autonomy or control?
      • No one-size-fits-all answer
  • Example:
    • Tesla's Autopilot system emphasizes driver vigilance and readiness to take control

An image of the Tesla logo.

AI Ethics

Navigating the challenges

  • Navigating ethical dilemmas in AI requires thoughtful trade-offs
  • Importance of human element in decision-making
  • Striving for better decisions in complex situations
  • Need for diverse stakeholders' involvement and continuous AI monitoring
AI Ethics

Let's practice!

AI Ethics

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